RNA secondary structured logic gates for profiling the microRNA cancer biomarkers

Deregulation of microRNAs expression is symptomatic of cancer disease and occurs before the awareness of cancer signs. Early detection of cancer disease can improve or drop the disease entirely. DNA computing is an emerging field of detecting microRNAs based on toehold‐mediated strand displacement reactions, which is a more efficient method than the commonly used method like real‐time PCR. Accuracy and cost of diagnostic applications are essential criteria that are achieved by using the DNA logic gates based on the DNA computing method. In this study, the authors proposed the multi‐input liver cancer biosensor with the RNA secondary structure motifs as the computational module and two approaches are suggested.Inspec keywords: cancer, biocomputing, biochemistry, DNA, RNA, biosensors, logic gates, liver, macromolecules, genetics, molecular biophysics, diseasesOther keywords: RNA secondary structured logic gates, microRNA cancer biomarkers, microRNAs expression, cancer disease, cancer signs, detecting microRNAs, toehold‐mediated strand displacement reactions, DNA logic gates, DNA computing method, multiinput liver cancer biosensor, RNA secondary structure motifs     

Evaluation of antimicrobial and dyeing properties of walnut (Juglans regia L.) green husk extract for cosmetics

Walnut green husk is one of the main waste products from walnut and could be used as a source of natural dyeing compounds such as juglone. The present study was conducted to evaluate the effective use of walnut green husk extract as a natural hair dye. Dyeing properties, fastness and antimicrobial behaviours of dyed hair and also a skin irritation test for natural hair dye on rat skin were examined. When the extract was mixed with ascorbic acid as a developer, ferrous sulphate as a mordant, and Aloe vera extract used as a secondary mordant and also a cosmetic ingredient, the reaction resulted in a dark‐brown colour on hair samples. The dyed hair exhibited appropriate colour strength having excellent morphology for a hair surface coated with dye molecules. In addition, the dyed hair possessed good resistance to washing and daylight fastness, without any irritant properties as shown in a rat model, although high concentrations of iron‐based mordant may be problematic for long‐term usage. This paper also suggests the use of natural mordants such as lactic and oxalic acids to avoid any probable risks. Walnut green husk extract was an appropriate natural hair dyeing agent in practice and showed maximum antimicrobial activity compared with semi‐synthetic and commercial hair dyes. The results demonstrated that walnut green husk can be used as an economical, valuable, eco‐friendly and safe source of dyeing and antimicrobial agents for cosmetic products.     

Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses

The determination of deformation modulus of rock masses is one of the most difficult tasks in the field of rock mechanics. Due to the high cost and measurement difficulties of in situ tests in modulus determination, the predictive models using regression based statistical methods, back propagation neural networks (BPNN) and fuzzy systems are recently employed for the indirect estimation of the modulus. Among these methods, the BPNN has been reported to be very useful in modeling the rock material behavior, such as deformation modulus, by many researchers. Despite its extensive applications, design and structural optimization of BPNN are still done via a time-consuming reiterative trial-and-error approach. This research focuses on the efficiency of the genetic algorithm (GA) in design and optimizing the BPNN structure and its application to predict the deformation modulus of rock masses. GA is utilized to find the optimal number of neurons in hidden layer, learning rates and momentum coefficients of hidden and output layers of network. Then the result is compared with that of trial-and-error procedure. For the purpose, a database including 120 data sets was employed from four dam sites and power house locations in Iran. Taking advantages of performance criteria such as MSE, MAE, r, proved that the GA-ANN model gives superior predictions over the trial-and-error model.     

Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network

We use genetic programming (GP) to determine the deformation modulus of rock masses. A database of 150 data sets, including modulus of elasticity of intact rock (Ei), uniaxial compressive strength (UCS), rock mass quality designation (RQD), the number of joint per meter (J/m), porosity, and dry density for possible input parameters, and the modulus deformation of the rock mass determined by a plate loading test for output, was established. The values of geological strength index (GSI) system were also determined for all sites and considered as another input parameter. Sensitivity analyses are considered to find out the important parameters for predicting of the deformation modulus of rock mass. Two approaches of sensitivity analyses, based on “statistical analysis of RSE values” and “sensitivity analysis about the mean”, are performed. Evolution of the sensitivity analyses results establish the fact that variable of UCS, GSI, and RQD play more prominent roles for predicting modulus of the rock mass, and so those are considered as the predictors to design the GP model. Finally, two equations were achieved by GP. The statistical measures of root mean square error (RMSE) and variance account for (VAF) have been used to compare GP models with the well-known existing empirical equations proposed for predicting the deformation modulus. These performance criteria proved that the GP models give higher predictions over existing empirical models.     

Estimation of deformation modulus of rock masses by using fuzzy clustering-based modeling

The paper describes a method which incorporates Takagi–Sugeno (TS) fuzzy modeling with two data clustering approaches including fuzzy-c-means (FCM) clustering and subtractive clustering to estimate the rock mass modulus of deformation. For this aim, a database including 120 cases collected from several galleries of dam sites locations was established. The information returned by fuzzy clustering was initially used to define the number of rules and antecedent membership functions and afterwards linear least squares estimation implemented to obtain fuzzy consequent parameters. An adaptive neuro-fuzzy inference system (ANFIS) was applied to modify the pre-determined TS clustering-based model structures to improve the generalization performance of those. For evaluation of the performance, root mean square error (RMSE) and variance account for (VAF) values have been utilized as performance criteria. It can be said, that ANFIS approach enhances the performances of fuzzy clustering-based models in predicting modulus of deformation of rock masses successfully.     

Turbulent mass transfer of Al2O3 and TiO2 electrolyte nanofluids in circular tube

Experimental study was performed to investigate turbulent mass transfer in straight circular tube. Electrochemical limiting diffusion current technique was used to measure the mass transfer coefficient in fully developed hydrodynamics and under developed mass transfer region. TiO₂ and γ-Al₂O₃ nanoparticles were added into the electrolyte solution (ES) to make electrolyte nanofluids (ENF). Measurements revealed that enhancement in mass transfer reaches 10 % in a 0.01 vol% γ-Al₂O₃/electrolyte nanofluid while 18 % in a 0.015 vol% TiO₂/electrolyte nanofluid relative to the base ES. Mass transfer coefficients increased with nanoparticles concentration up to an optimum concentration (0.01 % in γ-Al₂O₃/electrolyte nanofluid and 0.015 % in TiO₂/electrolyte nanofluid) while decreased by increasing nanoparticles concentration further. Enhancement ratio which is the ratio of the mass transfer coefficient of nanofluid to that of the base fluid was a function of nanoparticle concentration and was independent of Reynolds number. The mechanisms of nanoparticles Brownian motion and nanoparticles clustering were used to describe the behavior of the enhancement ratio in ENF.